Detecting missing values with isna() in Pandas - Mini Project: Build & Apply

Detecting missing values with isna()

📖 Scenario: You work as a data analyst for a small online store. You have a table of customer orders, but some information is missing. You want to find out which orders have missing data so you can fix or investigate them.

🎯 Goal: You will create a pandas DataFrame with order data, then use the isna() method to find missing values in the data.

📋 What You'll Learn

Create a pandas DataFrame with specific order data including some missing values

Create a variable to hold the result of detecting missing values using isna()

Print the DataFrame showing which values are missing

💡 Why This Matters

🌍 Real World

Detecting missing data is important in real-world data analysis to clean and prepare data for accurate results.

💼 Career

Data analysts and scientists often check for missing values to decide how to handle incomplete data before analysis.

1

Create the orders DataFrame

Import pandas as pd and create a DataFrame called orders with these exact columns and values:
'OrderID': [101, 102, 103, 104],
'Customer': ['Alice', 'Bob', None, 'David'],
'Amount': [250, None, 150, 300]

Pandas

# Import pandas and create the orders DataFrame with the given data
# Your code here

Hint

Use pd.DataFrame with a dictionary of lists for columns.

2

Create a variable to detect missing values

Create a variable called missing_values that stores the result of calling orders.isna().

Pandas

import pandas as pd

orders = pd.DataFrame({
    'OrderID': [101, 102, 103, 104],
    'Customer': ['Alice', 'Bob', None, 'David'],
    'Amount': [250, None, 150, 300]
})

# Create missing_values using orders.isna()
# Your code here

Hint

Use the isna() method on the orders DataFrame.

3

Check missing values in the DataFrame

Use a print() statement to display the missing_values DataFrame.

Pandas

import pandas as pd

orders = pd.DataFrame({
    'OrderID': [101, 102, 103, 104],
    'Customer': ['Alice', 'Bob', None, 'David'],
    'Amount': [250, None, 150, 300]
})

missing_values = orders.isna()

# Print the missing_values DataFrame
# Your code here

Hint

Use print(missing_values) to see the True/False table.

4

Display missing values clearly

Print the orders DataFrame and then print the missing_values DataFrame to compare the original data with missing value locations.

Pandas

import pandas as pd

orders = pd.DataFrame({
    'OrderID': [101, 102, 103, 104],
    'Customer': ['Alice', 'Bob', None, 'David'],
    'Amount': [250, None, 150, 300]
})

missing_values = orders.isna()

print(missing_values)

# Print the orders DataFrame to compare
# Your code here

Hint

Print both DataFrames one after the other to see missing values clearly.

Practice

1. What does the pandas.DataFrame.isna() function do?

easy

A. It converts all values to strings.

B. It fills missing values with zeros.

C. It removes rows with missing values.

D. It shows which values in the DataFrame are missing (NaN).

Solution

1. Step 1: Understand the purpose of isna()

   The isna() function checks each cell in a DataFrame and marks True if the value is missing (NaN), otherwise False.

2. Step 2: Compare options with function behavior

   Only It shows which values in the DataFrame are missing (NaN). correctly describes this behavior. Options B, C, and D describe other functions or actions.

3. Final Answer:

   It shows which values in the DataFrame are missing (NaN). -> Option D

4. Quick Check:

   isna() = marks missing values True [OK]

Hint: Remember: isna() marks missing spots True [OK]

Common Mistakes:

• Confusing isna() with fillna()
• Thinking isna() removes data
• Assuming isna() changes data types

2. Which of the following is the correct syntax to check missing values in a DataFrame named df?

easy

A. isna(df)

B. df.isna

C. df.isna()

D. df.isna[]

Solution

1. Step 1: Recall method call syntax in pandas

   Methods like isna() must be called with parentheses on the DataFrame object, so df.isna() is correct.

2. Step 2: Evaluate each option

   df.isna() uses correct syntax. df.isna misses parentheses, so it returns the method object, not the result. isna(df) is incorrect because isna is a method, not a standalone function. df.isna[] uses invalid bracket syntax.

3. Final Answer:

   df.isna() -> Option C

4. Quick Check:

   Method call needs parentheses () [OK]

Hint: Use parentheses to call isna(): df.isna() [OK]

Common Mistakes:

• Forgetting parentheses after isna
• Trying to call isna as a standalone function
• Using brackets instead of parentheses

3. Given the DataFrame:

import pandas as pd
data = {'A': [1, None, 3], 'B': [4, 5, None]}
df = pd.DataFrame(data)
print(df.isna())

What is the output?

medium

A. A B
0 False False
1 True False
2 False True

B. A B
0 True False
1 False True
2 False False

C. A B
0 False True
1 False False
2 True False

D. A B
0 False False
1 False True
2 True False

Solution

1. Step 1: Identify missing values in each column

   Column 'A' has None at index 1, so True there. Column 'B' has None at index 2, so True there. All other values are present (False).

2. Step 2: Match the True/False pattern to output

   A B 0 False False 1 True False 2 False True matches the pattern: index 1 in 'A' is True, index 2 in 'B' is True, others False.

3. Final Answer:

   A B 0 False False 1 True False 2 False True -> Option A

4. Quick Check:

   None values marked True by isna() [OK]

Hint: Look for None positions; isna() marks them True [OK]

Common Mistakes:

• Mixing row and column positions
• Confusing True and False values
• Assuming isna() replaces values

4. You run this code:

import pandas as pd
data = {'X': [10, 20, None], 'Y': [None, 40, 50]}
df = pd.DataFrame(data)
missing = df.isna

Why does missing.sum() cause an error?

medium

A. Because missing is a DataFrame, not a function.

B. Because isna is not called with parentheses when assigned.

C. Because isna() does not exist in pandas.

D. Because df is not defined.

Solution

1. Step 1: Understand method assignment without parentheses

   Assigning missing = df.isna stores the method itself, not the result. To get the result, you must call it with parentheses.

2. Step 2: Explain why missing.sum() causes error

   Assigning missing = df.isna stores the method object, not the DataFrame result. Method objects do not have a .sum() attribute--only DataFrames do. Thus, missing.sum() raises AttributeError.

3. Final Answer:

   Because isna is not called with parentheses when assigned. -> Option B

4. Quick Check:

   Assign method with () to get result [OK]

Hint: Assign with parentheses to get result, not method [OK]

Common Mistakes:

• Assigning method without parentheses and expecting result
• Confusing method object with method call
• Assuming isna() is not a pandas method

5. You have a DataFrame df with columns 'Age' and 'Salary'. You want to count how many missing values are in each column using isna(). Which code correctly does this?

hard

A. df.isna().sum()

B. df.isna.sum()

C. df.sum().isna()

D. df.isna().count()

Solution

1. Step 1: Use isna() to mark missing values

   The isna() method returns True for missing values and False otherwise.

2. Step 2: Sum True values per column to count missing

   Since True is treated as 1 and False as 0, summing over rows counts missing values per column. So df.isna().sum() gives the count per column.

3. Final Answer:

   df.isna().sum() -> Option A

4. Quick Check:

   Sum True from isna() to count missing [OK]

Hint: Sum isna() results to count missing per column [OK]

Common Mistakes:

• Forgetting parentheses after isna
• Using count() which counts all rows, not missing
• Applying sum() before isna()